ArticleMagnetic resonance in medicine2017
Are complex DCE-MRI models supported by clinical data?
Article in Magnetic resonance in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Dynamic Contrast Enhanced-Magnetic Resonance Fingerprinting (DCE-MRF) Improves Detection of Induced Vascular Perfusion Reduction in a Mouse Breast Cancer Model.Magnetic resonance in medicine · 2026Article
- Dynamic contrast enhanced-magnetic resonance fingerprinting (DCE-MRF): A new quantitative MRI method to reliably assess tumor vascular perfusion.Magnetic resonance in medicine · 2025Article
- Parametric maps of spatial two-tissue compartment model for prostate dynamic contrast enhanced MRI - comparison with the standard tofts model in the diagnosis of prostate cancer.Physical and engineering sciences in medicine · 2023Article
- Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer.Molecular imaging and biology · 2023Article
- Advanced MR Techniques for Preoperative Glioma Characterization: Part 1.Journal of magnetic resonance imaging : JMRI · 2023Review
- Surrogate vascular input function measurements from the superior sagittal sinus are repeatable and provide tissue-validated kinetic parameters in brain DCE-MRI.Scientific reports · 2022Article
- Non-Invasive Evaluation of Cerebral Microvasculature Using Pre-Clinical MRI: Principles, Advantages and Limitations.Diagnostics (Basel, Switzerland) · 2021Review
- Analysis Protocol for Dynamic Contrast Enhanced (DCE) MRI of Renal Perfusion and Filtration.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Dynamic Contrast Enhancement (DCE) MRI-Derived Renal Perfusion and Filtration: Basic Concepts.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Study on the Value of DCE-MRI in Differentiating Glossitis and Tongue Cancer and the Intratumour Heterogeneity.Cancer management and research · 2021Article
- Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer.Frontiers in oncology · 2021Article
- Imaging-based characterization of convective tissue properties.International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group · 2020Article
- Toward noninvasive quantification of adipose tissue oxygenation with MRI.International journal of obesity (2005) · 2020Article
- Article
- Clinical Implementation of a Free-Breathing, Motion-Robust Dynamic Contrast-Enhanced MRI Protocol to Evaluate Pleural Tumors.AJR. American journal of roentgenology · 2020Article
- Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network.IEEE transactions on medical imaging · 2020Article
- Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients.Frontiers in oncology · 2020Article
- Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.NMR in biomedicine · 2020Article
- Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models.Neuroradiology · 2019Article
- Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients.Magnetic resonance in medicine · 2019Article
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6 authors.
Funding
Abstract
purposeTo ascertain whether complex dynamic contrast enhanced (DCE) MRI tracer kinetic models are supported by data acquired in the clinic and to determine the consequences of limited contrast-to-noise.
methodsGenerically representative in silico and clinical (cervical cancer) DCE-MRI data were examined. Bayesian model selection evaluated support for four compartmental DCE-MRI models: the Tofts model (TM), Extended Tofts model, Compartmental Tissue Uptake model (CTUM), and Two-Compartment Exchange model.
resultsComplex DCE-MRI models were more sensitive to noise than simpler models with respect to both model selection and parameter estimation. Indeed, as contrast-to-noise decreased, complex DCE models became less probable and simpler models more probable. The less complex TM and CTUM were the optimal models for the DCE-MRI data acquired in the clinic. [In cervical tumors, Ktrans, Fp, and PS increased after radiotherapy (P = 0.004, 0.002, and 0.014, respectively)].
conclusionCaution is advised when considering application of complex DCE-MRI kinetic models to data acquired in the clinic. It follows that data-driven model selection is an important prerequisite to DCE-MRI analysis. Model selection is particularly important when high-order, multiparametric models are under consideration. (Parameters obtained from kinetic modeling of cervical cancer clinical DCE-MRI data showed significant changes at an early stage of radiotherapy.) Magn Reson Med 77:1329-1339, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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